Industry Research Finds Two-Thirds of Clinical Data Managers and CRAs Believe Current Inefficiencies Put Data Quality at Future Risk
Report shows completing manual data reconciliation, cleaning, and review takes each data manager more than 12 hours per week, per study

Each round of manual data review, cleaning, and reconciliation takes a data manager more than 12 hours per week, per study to complete. This is because nearly all respondents (97%) perform reconciliation outside of clinical systems or use a mix of systems to complete the process. The disconnected approaches increase the burden on clinical teams and the risk of poor data quality.
Key insights from the research on the state of clinical data management include:
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Automation is the number one priority for data managers. When asked how the data manager role will evolve over the next two years, 71% of respondents say they expect to be using more automation for data cleaning. Automated processes across technologies can shift effort from spreadsheets to strategic initiatives, like risk-based data management.
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CRAs want better documentation and tracking. The lack of connectivity across clinical systems requires CRAs to conduct manual validation of monitoring visits. Nearly half (44%) say improving documentation and tracking is their top priority.
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Complexity, resources, and resistance to change are barriers. The main challenges to efficiency include protocol complexity (58%), budget and resource constraints (57%), and resistance to change (48%). This shows an opportunity for clinical leaders to continue driving change for data managers and CRAs to work in new, more effective ways.
- Connected systems viewed as key to productivity. Most respondents (81%) believe connecting clinical systems would streamline study execution. The report shows 75% of data managers say their teams are in the process of modernizing compared to 57% of CRAs, yet many feel SOPs do not optimize use of available tools or align with real-world workflows, highlighting a gap that can prevent progress.
"The risk of poor data quality spans far beyond a monitoring visit or listing review, potentially impacting regulatory submission success. The research shows that the people executing studies need change and are asking for simpler processes and automation for more efficient clinical trials," said
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